Metabolic engineering in the host Yarrowia lipolytica

Author’s Accepted Manuscript Metabolic engineering in the host Yarrowia lipolytica Ahmad M. Abdel-Mawgoud, Kelly A. Markham, Claire M. Palmer, Nian Liu, Gregory Stephanopoulos, Hal S. Alper www.elsevier.com/locate/ymben

PII: DOI: Reference:

S1096-7176(18)30273-8 https://doi.org/10.1016/j.ymben.2018.07.016 YMBEN1445

To appear in: Metabolic Engineering Received date: 3 July 2018 Revised date: 23 July 2018 Accepted date: 24 July 2018 Cite this article as: Ahmad M. Abdel-Mawgoud, Kelly A. Markham, Claire M. Palmer, Nian Liu, Gregory Stephanopoulos and Hal S. Alper, Metabolic engineering in the host Yarrowia lipolytica , Metabolic Engineering, https://doi.org/10.1016/j.ymben.2018.07.016 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Metabolic engineering in the host Yarrowia lipolytica Ahmad M. Abdel-Mawgouda,1, Kelly A. Markhamb,1, Claire M. Palmerc,1, Nian Liua,1, Gregory Stephanopoulosa,*, Hal S. Alperb,c,* a

Department of Chemical Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge, Massachusetts 02139

b

McKetta Department of Chemical Engineering The University of Texas at Austin 200 E Dean Keeton St. Stop C0400 Austin, Texas 78712

c

Institute for Cellular and Molecular Biology The University of Texas at Austin 2500 Speedway Avenue Austin, Texas 78712 E-mail: [email protected] E-mail: [email protected] *

*

Co-Corresponding author: Phone: (617) 253-4583,Mailing Address: Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139

Co-Corresponding author: Phone: (512) 471-4417, Fax: (512) 471-7060, Mailing Address: 200 E Dean Keeton St. Stop C0400 Austin, TX 78712

Abstract The nonconventional, oleaginous yeast, Yarrowia lipolytica is rapidly emerging as a valuable host for the production of a variety of both lipid and nonlipid chemical products. While the unique genetics of this organism pose some challenges, many new metabolic engineering tools have emerged to facilitate improved genetic manipulation in this host. This review establishes a case for Y. lipolytica as a premier metabolic engineering host based on innate metabolic capacity, emerging synthetic tools, and engineering examples. The metabolism underlying the lipid accumulation phenotype of this yeast as well as high flux through acyl-CoA

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Authors contributed equally to this work

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precursors and the TCA cycle provide a favorable metabolic environment for expression of relevant heterologous pathways. These properties allow Y. lipolytica to be successfully engineered for the production of both native and nonnative lipid, organic acid, sugar and acetylCoA derived products. Finally, this host has unique metabolic pathways enabling growth on a wide range of carbon sources, including waste products. The expansion of carbon sources, together with the improvement of tools as highlighted here, have allowed this nonconventional organism to act as a cellular factory for valuable chemicals and fuels. Keywords: Yarrowia lipolytica, metabolic engineering, synthetic biology, oleochemicals, biofuel 1. Introduction Yarrowia lipolytica is emerging as the model non-conventional oleaginous yeast. As an organism with “generally regarded as safe” (GRAS) status (Groenewald et al., 2014), it has been widely recognized as a potential industrial workhorse for the production of lipid-based biofuels and oleochemicals (Markham et al., 2017). In particular, Y. lipolytica is well suited for industrial production of oleochemicals as wildtype strains can accumulate lipids up to 70% of dry biomass (Beopoulos and Nicaud, 2012). Key metabolic traits that contribute to this oleaginous phenotype include high acetyl-CoA flux, high tricarboxylic acid (TCA) cycle flux, and lack of fermentative capacity (Christen and Sauer, 2011; Kavšček et al., 2015). Moreover, Y. lipolytica has the ability to utilize diverse protein and hydrophobic substrates, which may be provided as cheap renewable carbon sources, and it grows at a wide range of pH and salinity conditions (Bankar et al., 2009; Michely et al., 2013). With these unique metabolic traits and recently developed metabolic engineering tools, this industrial host shows great promise for economic and renewable production of a plethora of

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new products in the future. As such, this article reviews Y. lipolytica research, discussing the current understandings of its biology as well as achievements in the metabolic engineering of this organism. We first introduce basic microbiological features of Y. lipolytica and their relevance to metabolic engineering. Next, we discuss the genetic engineering and systems biology tools that are available for this organism to date. Following this, we discuss past successes that demonstrate the full potential of Y. lipolytica as a host for microbial production of lipid derived chemicals, organic acids, sugar alcohols, and acetyl-CoA derived products. Additionally, we highlight improvements in the metabolic engineering for diverse carbon utilization. Finally, we conclude with perspectives on the future of engineering in Y. lipolytica.

2. Y. lipolytica background and basics As natural niches for Y. lipolytica, this organism is readily found in lipid and/or protein rich substrates, such as cheeses (e.g. Camembert and blue-veined cheeses), dairy products (Roostita and Fleet, 1996), as well as meat and sausages (Fickers et al., 2005; Groenewald et al., 2014; Jacques and Casaregola, 2008). This organism is found to live in soil, sewage, and oilpolluted environments (Hassanshahian et al., 2012). These chemical habitats align well with an efficient metabolic capacity to hydrolyze lipids via secreted lipases and esterases, assimilate hydrocarbons and fatty acids via terminal, β-, and ω-oxidation pathways (Abghari and Chen, 2014; Thevenieau et al., 2010), as well as express and secrete extracellular proteases (Fabre et al., 1991). This efficient protein degradation and hydrophobic substrate catabolism seen in Y. lipolytica is supported by a host of genes encoding essential functions (Gaillardin et al., 2013). For example, Y. lipolytica has 16 acylglycerol lipases, 12 cytochrome P450s for hydrocarbon and fatty acid oxidation, 14 fatty acid transporters, as well as 38 aspartyl proteases and 15 serine

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proteases (Gaillardin et al., 2013). As a result, these traits make this yeast an especially interesting organism to study and engineer.

2.1 Taxonomy and morphology Y. lipolytica, formerly classified as Candida lipolytica or Saccharomycopsis lipolytica (Esser and Stahl, 1976), is a yeast belonging to the Ascomycota phylum, Dipodascaceae family, Fungi kingdom (Kurtzman, 2011). It is denoted as a “non-conventional yeast” with respect to its distinctive genome structure and its large phylogenetic distance relative to other yeasts while sharing common properties with molds and higher eukaryotes (Barth and Gaillardin, 1996; Barth and Gaillardin, 1997; Dujon et al., 2004; Torres-Guzmán and Domínguez, 1997). Y. lipolytica is a heterothallic yeast commonly found in nature in either of the two haploid mating types, MatA or MatB, although diploid strains were occasionally isolated (Knutsen et al., 2007). With regard to morphology, dimorphism is one of the characteristic features of Y. lipolytica. Specifically, this organism can exist in either the yeast form or the pseudo-/septate hyphae (non-branching) forms (Barth and Gaillardin, 1996; Kawasse et al., 2003). Yeast-tohyphae ratio partly contributes to the diverse colony morphologies of Y. lipolytica and is affected by medium and growth conditions as well as by genetic factors (Barth and Gaillardin, 1996). As a result, regulation of yeast-to-hyphae transition is suggested to be complex and multifactorial and induced as a survival mechanism in response to stressful environmental and nutritional conditions (Bellou et al., 2014; Gancedo, 2001; Hurtado et al., 2000; Morales-Vargas et al., 2012; O'Shea and Walsh, 2000; Pérez-Campo and Domínguez, 2001; Ruiz-Herrera and Sentandreu, 2002; Torres-Guzmán and Domínguez, 1997). The morphological state of Y. lipolytica has profound impacts on bioreactor performance. Since yeast forms are associated with

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better rheological properties, they are preferred to hyphal forms in submerged fermentation (Timoumi et al., 2017). On the other hand, hyphae forms may be desired in solid-state fermentations (Bellou et al., 2014). In a laboratory setting, colonies of Y. lipolytica are of creamy texture and show a convoluted pale white matte surface (Figure 1), whereas traditional yeast form rich colonies appear smooth and glistening (Heslot, 1990). Under a normal light microscope, cells of Y. lipolytica appear spherical, ellipsoidal, or elongated with typical dimensions of 3-5 X 3.3-15 µm and are arranged singly, in pairs, or clustered in groups (Kurtzman, 2011) (Figure 2). One particularly characteristic feature of Y. lipolytica is its possession of large intracellular lipid bodies. Lipid bodies, also called oleosomes, are neutral-lipid storage organelles (0.65 to 2.5 µm) (Athenstaedt, 2010) that are easily discernable using fluorescence microscopy (Figure 2) (Athenstaedt, 2010). In this organism, the lipid bodies are comprised mainly of triacylglycerides (TAG) as the inner core and, to a lesser extent, steryl esters (SE) as the outer core (Athenstaedt et al., 2006). The entire structure is encapsulated by a protein-embedded phospholipid monolayer (Beopoulos et al., 2009; Czabany et al., 2008). In general, lipid body formation is induced under unfavorable nutritional conditions, including nitrogen limitation or limited oxygen supply (Kavšček et al., 2015). These conditions direct metabolism towards energy storage as TAGs, which is part of a complex system of fatty acid homeostasis (Garay et al., 2014). Moreover, lipid body formation is suggested to play a role in free fatty acid detoxification by segregating these compounds away from the general cytosol and into a membrane-bound compartment (Athenstaedt, 2010). Overall, lipid bodies serve as dynamic storage organelles since the yeast can remobilize fatty acids out of lipid bodies by TAG lipases

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and steryl ester hydrolases in response to its needs for biomass precursors and energy supply (Abghari and Chen, 2014).

2.2 Growth conditions Y. lipolytica is a strictly aerobic, non-fermentative yeast (Barth and Gaillardin, 1996) and thus requires good aeration for optimal growth, a potential limitation in some industrial fermentation settings. The recommended temperature for cultivation is 25-30 °C, but growth can occur at refrigeration temperature (Fickers et al., 2005; Kurtzman, 2011) and rarely even up to 34 °C (Barth and Gaillardin, 1996). Its inability to grow at temperatures above 34 °C and limited capacity to cause only mild, self-limiting infections in severely immunocompromised patients are amongst the reasons behind its GRAS status (Groenewald et al., 2014; Jacques and Casaregola, 2008). Y. lipolytica is exceedingly tolerant to a wide range of pH values, including highly acidic environments (Egermeier et al., 2017). However, it is interesting that across this pH range, the metabolic (Egermeier et al., 2017) and morphologic (Timoumi et al., 2017) profiles differ significantly. In terms of carbon sources, Y. lipolytica has a characteristic ability to assimilate a wide range of unusual hydrophobic and hydrophilic carbon sources. Hydrophilic carbon sources include two monosaccharides, namely glucose and fructose (Michely et al., 2013); the amino sugar N-acetyl-D-glucosamine; some alcohols, such as ethanol (up to 3%) but not methanol; many polyols, e.g. glycerol, erythritol, mannitol, glucitol; many organic acids, e.g. acetate (up to 0.4%), lactate, succinate and citrate(Barth and Gaillardin, 1997). The hydrophobic substrates that can be utilized by Y. lipolytica are alkanes (C12 to C16 but not C8 and C10), alkenes (Barth and Gaillardin, 1996; Kurtzman, 2011), fatty acids, fatty acid methyl esters, and triglycerides

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(Michely et al., 2013). Amongst these carbon sources is the ability to utilize crude glycerol, an extremely low cost ($ 0.05 per pound) byproduct of the oil and biodiesel industries (Yang et al., 2012). Additional waste products including used frying oils have been explored as alternative, cheap carbon sources (Domínguez et al., 2010). Further engineering efforts (described later) are expanding the array of carbon sources. As for nitrogen sources, Y. lipolytica assimilates inorganic ammonium (but not nitrates), usually in the chloride or sulfate salt forms (Fickers et al., 2004; Kurtzman, 2011). More options are available for Y. lipolytica with regard to organic nitrogen sources including both defined ones, such as urea, and complex ones, such as casamino acids, peptones, tryptones, and yeast extract (Fickers et al., 2004). Interestingly, human urine, which contains urea at 1-2% w/v, has been showed to be an effective and cheap nitrogen source for Y. lipolytica (Brabender et al., 2018). In general, the nature and concentration of nitrogen source play a significant role in growth and lipid production in Y. lipolytica as discussed later. Vitamin requirements are also an important consideration in the cultivation of strains. Y. lipolytica is auxotrophic to thiamine (vitamin B1) only, unlike other members of the Yarrowia clade which are auxotrophic to multiple vitamins (Barth and Gaillardin, 1996; Kurtzman, 2011). More simply, Y. lipolytica can grow in minimal medium without YNB added, but requires supplementation of thiamine (at 0.4-1 mg/L) together with some trace elements (Egermeier et al., 2017; Jost et al., 2015). No studies have systemically investigated, however, the complete trace element requirements of Y. lipolytica (Gasmi et al., 2011). A good starting point for such a study could be inspired, however, from the composition of trace elements provided in the commercial trace elements solution 2 (Celińska and Grajek, 2013) or YNB in order to develop a standard, less expensive defined minimal medium.

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3. Genetics The metabolic engineering of any microorganism often requires genetic manipulations, which cannot be done without a detailed understanding of its genome structure as well as its gene expression machinery. Y. lipolytica is no exception, and indeed, the community has accumulated considerable knowledge about its genetics. This section is dedicated to the review of relevant genetic information in Y. lipolytica.

3.1 Genomic elements The genome of Y. lipolytica is composed of 6 nuclear chromosomes (A to F) (Dujon et al., 2004; Pomraning and Baker, 2015) and 1 mitochondrial chromosome (Kerscher et al., 2001). Both haploid and diploid strains of Y. lipolytica propagate stably in laboratory settings (Knutsen et al., 2007), with haploid strains more favored in lab settings. The genomes of the three widely studied strains of Y. lipolytica, CLIB122 (Dujon et al., 2004), W29 (CLIB89) (Pomraning and Baker, 2015), and PO1f (Liu and Alper, 2014) together with certain derivative strains have been sequenced and made available at GeneBank of the NCBI website (https://www.ncbi.nlm.nih.gov/genome/genomes/194). The genome sequence of Y. lipolytica CLIB122 is also found through the website of Genome Resources for Yeast Chromosomes (http://gryc.inra.fr/index.php?page=home). The total genome size of Y. lipolytica strains CLIB122 or W29, including the mitochondrial chromosome, is 20.5 Mb with a net G-C content of 49%, which are 1.7 and 1.28 times larger than those of S. cerevisiae, respectively (Dujon et al., 2004).

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Genome annotations of CLIB122 and W29 show a total of 7144 and 8746 genes, respectively. Of these genes, 6472 and 7949 are protein-coding genes (open reading frames, ORFs) in CLIB122 and W29, respectively. Such numbers of ORFs are unexpectedly low given the remarkably larger genome size of Y. lipolytica (20.5 Mb) compared to that of S. cerevisiae (12 Mb) (Dujon et al., 2004). This obviously reflects on a lower gene density—a value that is 46.3% in Y. lipolytica (i.e. 1 gene/3 Kb) compared to 70.3% (1 gene/2 Kb) in S. cerevisiae (Dujon et al., 2004). These large intergenic regions have allowed for identification of 11 chromosomal sites suitable for gene integration in intergenic regions surrounded by highly expressed neighboring genes, according to a previous transcriptomic study (Morin et al., 2011), while distant enough from these native neighboring genes not to cause any possible perturbation to their function (Holkenbrink et al., 2018). A particularly important component of the chromosomal DNA for metabolic engineering in Y. lipolytica is the ribosomal DNA (rDNA), as it is often targeted for homology-based multicopy gene integration. Y. lipolytica has 7 subtelomeric loci of dispersed rDNA repeats, which is distinctive than most hemiascomycetes where there are only 1 to 3 intrachromosomal clustered rather than dispersed rDNA repeat (Dujon et al., 2004). At least 105 copies of 5S rDNA are dispersed throughout the genome of Y. lipolytica (Dujon et al., 2004). Previously, there was an attempt to use these rDNA repeats (8.7 Kb) to construct multicopy gene integration vectors (Juretzek et al., 2001; Le Dall et al., 1994). However, the method proved ineffective due to concerns regarding the number and stability of gene copies in the genome (Le Dall et al., 1994). Nonetheless, this approach has been used effectively to engineer these cells for improved production of products (Blazeck et al., 2013a; Celińska and Grajek, 2013; Liu et al., 2017a).

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As mentioned before, one chromosome of Y. lipolytica resides in the mitochondrion. The mitochondrial DNA (mtDNA) of Y. lipolytica is 47.9 Kb long and codes for the 14 hydrophobic subunits of respiratory chain complexes, the large and small rRNAs, and 27 tRNAs (Kerscher et al., 2001). Being a strictly aerobic non-fermentative yeast, Y. lipolytica shows the most powerful translational efficiency of mitochondrial genes related to aerobic respiration (Man and Pilpel, 2007) and a strong TCA cycle flux (Christen and Sauer, 2011). Accordingly, Y. lipolytica cannot survive mitochondrial mutations affecting respiratory chains and hence this yeast is classified as a petite mutant-negative species (Bakkaiova et al., 2014; Savická and Šilhánková, 1995). The high-expression genetic environment of mtDNA provide an optimum place for integration of heterologous genes that are required to be expressed in this organelle. Nonetheless, genetic manipulations of mtDNA is still challenging (Gammage et al., 2017). Recently, several successful trials in Y. lipolytica (Isakova et al., 2015) have provided promise for more efficient mitochondrial genome engineering in Y. lipolytica.

3.2 Translational machinery Regarding its translational machinery, Y. lipolytica possesses 510 genes coding for tRNA, which is the largest number amongst yeasts and 1.86 times higher than that of S. cerevisiae (Dujon et al., 2004). In Y. lipolytica, the tRNA genes code for the same 44-tDNA set as higher eukaryotes. Uniquely, among all eukaryotic systems, 26 of these tRNA contain introns in Y. lipolytica (Dujon et al., 2004; Neuvéglise et al., 2013). More interestingly, Y. lipolytica harbors ~50 copies of hybrid dicistronic co-transcribed tRNA-5S rRNA genes (Neuvéglise et al., 2013) in addition to the solo tRNA and tandem-tRNA (di-cistronic) conventionally found in hemiascomycetes (Neuvéglise et al., 2005). This implies that Y. lipolytica possesses an efficient

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and unique tRNA processing/maturation machinery which was recently exploited in engineering tRNA-gRNA fusion for CRISPR/Cas9-mediated genome editing in Y. lipolytica (Schwartz et al., 2016).

3.3 mRNA processing Hemiascomycetes are generally intron-deficient compared to higher eukaryotes (Stajich et al., 2007). Nonetheless, Y. lipolytica has the most intron-rich genome in yeasts with at least 16% of its genes containing introns (Mekouar et al., 2010). Interestingly, the length and number of these introns per gene vary remarkably across the genome. As such, the average spliceosomal intron length in Y. lipolytica is ~280 bp (Stajich et al., 2007). Yet, there are 16 introns with 1 to 3.4 Kb whereas 300 introns are less than 100 bp (Gaillardin et al., 2013). As for the number of introns per single gene, 87 genes contain multiple introns in Y. lipolytica which is more than 20 times higher than that in S. cerevisiae (where only 4 genes have multiple introns) (Thevenieau et al., 2009). In Y. lipolytica, mRNA splicing proceeds via both conventional (Gaillardin et al., 2013; Stajich et al., 2007) and alternative splicing (Gaillardin et al., 2013; Neuvéglise et al., 2005). Interestingly, intron-containing genes can show higher expression profiles than intronless versions of the same genes (Le Hir et al., 2003) as demonstrated recently (Hong et al., 2012; Tai and Stephanopoulos, 2013). Specifically, one study showed a 5-fold increase in the expression of β-glucuronidase gene under the fructose-bisphosphate aldolase-1 promoter (pFBA1) compared to the intronless version of the same gene (Hong et al., 2012). Another study reported a 17-fold increase in expression of the intron-containing β-galactosidase gene under translation elongation factor-1α promoter (pTEF1in) compared to the intronless version of the same gene (Tai and

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Stephanopoulos, 2013). The abundance of introns present in Y. lipolytica leads to interesting queries to improve our future metabolic engineering efforts in this host.

3.4 DNA repair and recombination Similar to other eukaryotes (Ma et al., 2003), mechanisms involved in DNA doublestrand break (DSB) repair in Y. lipolytica include homology-directed recombination (HR) (Richard et al., 2005) and non-homologous end joining (NHEJ) (Daley et al., 2005; Richard et al., 2005). Although Y. lipolytica is capable of both modes of DNA repair, NHEJ is the dominant form of DNA repair (Verbeke et al., 2013) meaning that donor DNA is randomly integrated into the genome. From a genetic engineering point of view, a more specific loci targeting is preferred to decrease the heterogeneity that occurs with random integration (Madzak and Beckerich, 2013). NHEJ is enabled by the heterodimer protein complex Ku70/Ku80 (Lustig, 1999). Knockout of the genes encoding this complex resulted in an increased rate of selected cells that had undergone HR in Y. lipolytica (Kretzschmar et al., 2013; Verbeke et al., 2013; Bredeweg et al., 2017). Recently, CRISPR-Cas9 mediated gene integration technology has been adapted to Y. lipolytica (Gao et al., 2016b; Schwartz et al., 2016) and was associated with HR rate of about 50 to 70% (Schwartz et al., 2017b). These methods facilitate the isolation of cells with locusspecific modifications, improving precision of genetic engineering in Y. lipolytica.

4. Metabolic engineering and synthetic biology tools Developing efficient and predictable synthetic biology tools is essential to advancing the scope of metabolic engineering possibilities in any organism. In this section, we discuss the genetic tools that have been developed that help facilitate metabolic engineering in Y. lipolytica. 12

We then describe control elements that allow for modulating gene expression. Following control strategies, we discuss advances in emerging tools like CRISPR-Cas9. Finally, we demonstrate the development of effective models that will guide future strain design efforts. Collectively, these tools will enable metabolic engineering efforts catapulting Y. lipolytica chemical production to industrial successes.

4.1 Basic genetic tools As a non-conventional yeast, enabling metabolic engineering of Y. lipolytica has required the non-trivial development of basic genetic tools. Due to its propensity for NHEJ, traditional methods for gene expression on episomal plasmids are limited, but we discuss the vectors that have been developed for this purpose. We investigate optimized transformation methods allowing for the integration of exogenous DNA. Finally, we report on the selectable markers that have been effectively used in Y. lipolytica.

4.1.1 Episomal plasmids have been developed, but are generally unstable As a first step in making Y. lipolytica genetically tractable, episomal plasmids were investigated. Episomal plasmids are convenient tools for rapid genetics, but Y. lipolytica is devoid of any identifiable native plasmid (Heslot, 1990). Not surprisingly, Y. lipolytica thus still struggles to maintain any artificially transformed replicative plasmids (especially at high copy) despite efforts from several groups. An early patent describes an autonomously replicating sequence (ARS) that is effective in Y. lipolytica although unstable in mitosis (Fournier et al., 1993b). Further efforts demonstrated that two previously discovered ARS sequences (Fournier et al., 1993a; Fournier et al., 1991) were actually comprised of two essential elements--an origin of

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replication and a centromere (Vernis et al., 1997; Vernis et al., 2001). These ARS sequences are quite large compared to other hosts and thus more minimal regions have been explored (Yamane et al., 2008a; Yamane et al., 2008b). Recent work has improved copy number by 80% through fusing promoters upstream of the centromeric region to enable a dynamic expression range of 2.7-fold (Liu et al., 2014). Overall, stable episomal plasmid expression is still strongly limiting in this host.

4.1.2 Transformation Given the limitations of episomal plasmid expression there has been a focus on improving protocols for integrative transformation. While these methods can be used to transform episomal plasmids, the transformation efficiencies reported here refer specifically to integrative transformations. Y. lipolytica uptakes exogenous DNA with moderate efficiency compared to S. cerevisiae, which can regularly achieve transformation efficiencies exceeding 107 transformants per µg DNA (Kawai et al., 2010). Most transformation methods in Y. lipolytica use a chemical transformation technique starting with a lithium acetate treatment, followed by incubation in polyethylene glycol (PEG), dithiothreitol (DTT), and DNA at an elevated temperature. An early protocol uses this lithium acetate treatment, but requires an excessive amount of sonicated carrier DNA to achieve 104 transformants per µg DNA per 108 viable cells (Davidow et al., 1985). Without carrier DNA, this protocol results in less than 20 transformants (Davidow et al., 1985). Later studies have optimized this method by developing a one-step protocol where cells are directly prepared from a YPD plate (Chen et al., 1997), increasing site specific integration by using a strain with a zeta docking platform (Bordes et al., 2007), and adapting this protocol for high-throughput transformation in a 96-well plate (Leplat et al., 2015). 14

Each of these methods reports maximum transformation efficiencies generally ranging from 103 – 104 transformants per µg DNA, but suffer from limited efficiency for site-directed integration due to Y. lipolytica’s natural propensity for NHEJ. In addition to chemical transformation methods, an electroporation method for Y. lipolytica transformation has been developed with efficiencies up to 104 transformants per µg DNA (Wang et al., 2011). Collectively, these results demonstrate the genetic tractability of Y. lipolytica, though there is room to improve these transformation efficiencies, specifically for site-directed integrations.

4.1.3 A plethora of markers allow for incorporation of exogenous DNA in Y. lipolytica To offset the lack of strong episomal elements, a variety of different selective markers have been developed for use in Y. lipolytica genome engineering including both auxotrophic and dominant selection schemes. Straightforward auxotrophic selection is well documented in Y. lipolytica (Barth and Gaillardin, 1996; Madzak et al., 2004). In particular, the commonly used PO1 series of Y. lipolytica strains provide useful auxotrophies (Madzak et al., 2000). Additionally, a defective URA3 has been developed that allows for multi-copy integrations using a single transformation, up to 60 copies (Le Dall et al., 1994). Although Y. lipolytica is natively resistant to many commonly used antibiotics (Gaillardin and Ribet, 1987), dominant selection schemes have been reported for resistance to phleomycin, hygromycin, nourseothricin, chlorimuron ethyl, and mycophenolic acid (Wagner et al., 2018b). An alternative dominant marker has been developed that allows for selection with sucrose through expression of the SUC2 gene from S. cerevisiae (Nicaud et al., 1989). Together, this abundance of selection schemes allow for incorporation of exogenous DNA into Y. lipolytica

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enabling complex pathway engineering. Moreover, marker rescue systems like Cre-lox recombination have been implemented in this host (Fickers et al., 2003). Likewise, a collection of vector sets and associated DNA assembly tools have been described and reviewed in recent years (Holkenbrink et al., 2018; Celińska et al., 2017; Gao et al., 2014; Markham and Alper, 2018; Wong et al., 2017). Collectively, these basic genetic tools lay the foundation for all metabolic engineering in Y. lipolytica.

4.2 Control elements of gene expression After enabling metabolic engineering with the basic genetic tools reported above, modulation of expression is achievable through control elements like promoters and terminators. At the most basic level, native promoters have different strengths leading to a variety of different expression levels. A common strategy for increasing promoter strength, particularly for Y. lipolytica, is to identify and concatenate upstream activating sequences in front of a core promoter (Blazeck et al., 2011; Blazeck et al., 2013b; Madzak et al., 1999; Madzak et al., 2000). Adding an additional level of control is achievable by hybridizing inducible elements such as for oleic acid (Shabbir Hussain et al., 2016; Shabbir Hussain et al., 2017), n-decane (Sumita et al., 2002), and erythritol (Trassaert et al., 2017). An added level of control is achievable by using synthetic terminators (Curran et al., 2015). For further information, control elements for Y. lipolytica have been recently reviewed in greater detail (Markham and Alper). Collectively, these control elements allow for modulating gene expression to enable more complex metabolic engineering strategies.

4.3 Emerging tools for rapid engineering

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A series of emerging tools have demonstrated the potential of synthetic biology to revolutionize metabolic engineering efforts. First, we discuss how CRISPR-Cas9 has changed the scope of engineering possibilities in Y. lipolytica. Then, we investigate the utility of transposase systems for enabling multi-copy integrations and functional genomics. Together, these systems demonstrate the ever-evolving future of metabolic engineering in Y. lipolytica.

4.3.1 CRISPR-Cas9 is a powerful tool for metabolic engineering in Y. lipolytica Recent advances in CRISPR-Cas9 based genome engineering are beginning to improve prospects for engineering Y. lipolytica. Specifically, CRISPR-Cas9 was first implemented by Schwartz et al. through the use of an RNA polymerase II promoter or synthetic RNA polymerase III promoter with hammerhead and HDV ribozymes to drive sgRNA expression enabling single gene disruptions of pex10, mfe1, and ku70 with high efficiency (Schwartz et al., 2016). This work was later expanded to demonstrate multiplex editing of up to three targeted gene disruptions (Gao et al., 2016b) and more recently five genomic loci were identified for gene integration (Schwartz et al., 2017b). As an alternative approach, similar gene disruptions have been successfully achieved using T7-transcribed sgRNAs with editing efficiencies of up to 60% (Morse et al., 2018). These initial advances have sparked interest in using a deactivated Cas9 (dCas9) system for gene expression modification. To this end, four different repressors were used to establish a CRISPR interference (CRISPRi) system to repress gene expression in Y. lipolytica (Zhang et al., 2018) that efficiently repressed expression of GFP as well as multiplexed repression of a 3-gene pathway that creates a violet pigment (Zhang et al., 2018). Likewise, a descriptive protocol for genome editing and repression with CRISPR-Cas9 and –dCas9 has been recently published

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(Schwartz and Wheeldon, 2018). Finally, a VPR-dCas9 fusion was used to activate transcription of hrGFP and two native β-glucosidase genes (with multiplexed activation) demonstrating CRISPR activation (CRISPRa) (Schwartz et al., 2018). Collectively, these works demonstrate the wide utility of CRISPR systems in this host.

4.3.2 Transposases demonstrate unique potential for metabolic engineering Transposable elements (both native and heterologous) can be used for metabolic engineering applications. A diverse set of native transposable elements belonging to the retrotransposon (class I) and DNA transposon (class II) families have been identified in different strains of Y. lipolytica (Casaregola et al., 2000) including Ylt1, Tyl3 and Tyl6, belonging to the long terminal repeats (LTR or Zeta) group as well as Ylli from the non-LTR group (Casaregola et al., 2000; Magnan et al., 2016). An example of a DNA transposon is the Mutator-Like DNA transposon, Mutyl and Fotyl elements (Casaregola and Barth, 2013; Casaregola et al., 2000; Magnan et al., 2016; Neuvéglise et al., 2005). In some cases, these transposons may occur multiple times in the genome as is the case with Ylt1 which has 27 copies (partial and/or complete) in the genome of Y. lipolytica CLIB122 (Magnan et al., 2016). Such repetitive transposon systems can be used for targeting and amplifying copy number of transgenes (Juretzek et al., 2001; Le Dall et al., 1994; Madzak and Beckerich, 2013; Nicaud et al., 2003; Nicaud et al., 2002; Pignède et al., 2000; Schmid-Berger et al., 1994). As an example, a recent study used a DNA cut-and-paste transposon to amplify the copy number of a rate-limited polyketide synthase in Y. lipolytica (Yu et al., 2018). In addition to being useful for enhancing copy number, transposases (especially heterologous) can be used to create insertional mutagenesis libraries. One recent study

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demonstrated an adaption of the piggyBac transposon system that is commonly used in eukaryotic cells and established effective generation of genome-wide mutagenesis libraries and scarless genomic modifications in Y. lipolytica (Wagner et al., 2018b). An additional functional genomics study created a saturated mutagenesis library of the entire genome using a Hermes DNA transposon allowing for identification of essential genes, evaluation of two genome-scale models, and selection of high-lipid strains (Patterson et al., 2018). Collectively, these studies demonstrate the unique utility of transposable elements for metabolic engineering in this yeast.

4.4 Multi-omics data and computational models facilitate rational engineering in Y. lipolytica As a final set of tools for Y. lipolytica, computational approaches have been established to model metabolism. Initial advances have focused on using models to fundamentally understand and improve lipogenesis. One group used lipidomics, metabolomics, and RNA-seq to study nitrogen starvation, identifying transcriptional regulators and demonstrating that regulation of lipid formation occurs through regulation of amino acid biosynthesis (Kerkhoven et al., 2016). Further transcriptomic work identified and validated additional transcription factors regulating lipogenesis (Trebulle et al., 2017). The effects of nitrogen starvation were modeled through the development of two mathematical models for lipid production based on Monod and inhibition kinetics as well as the Droop quota model approach (Robles-Rodriguez et al., 2018). Here, researchers used experimental data for calibration and validation and developed interesting controls for carbon to nitrogen ratio (Robles-Rodriguez et al., 2018). Finally, a more comprehensive stoichiometric model containing 645 genes, 1083 metabolites, and 1471 reactions was compiled from older databases and coupled with two knockout algorithms, OptGeneKnock

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and IdealKnock, and an overexpression algorithm (APGC) to identify targets for TAG accumulation (Wei et al., 2017). These initial studies were capable of identifying targets to improve lipid formation in Y. lipolytica and serve as a platform to build models for other potential metabolites. Beyond lipid accumulation, in silico models have been applied for lycopene production (Zhao et al., 2017) and dodecanedioic acid production (Mishra et al., 2018). Thus, the use of big data is improving the ability to use rational engineering strategies to further improve this host. In conclusion, the synthetic and systems biology tools discussed here have paved the way to production of a variety of different chemicals through metabolic engineering. In the sections that follow, we demonstrate the wide applicability of these tools (when coupled with native metabolic capacities) to create chemicals including lipids, organic acids, sugar alcohols, and nonnative acetyl-CoA derived molecules. In the future, improvements of these tools (and development of novel tools) will facilitate efficient engineering efforts in Y. lipolytica.

5. The oleaginous phenotype and engineered lipid production of Y. lipolytica As has been alluded to above, Y. lipolytica has a distinctive metabolism when compared with the model yeast S. cerevisiae. While much information can be gained through comparison of these two organisms (Paiva et al., 2004; Beopoulos et al., 2008; Beopoulos et al., 2009; Christen and Sauer, 2011), it is the differences between these two organisms that make Y. lipolytica a valuable production host for a wide range of chemicals. In particular, as an oleaginous yeast, this organism has unique capabilities to synthesize high levels of intracellular lipids—the focus of many metabolic engineering approaches.

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5.1 Lipid metabolism Key to the oleaginous phenotype of Y. lipolytica is this organism’s ability to synthesize de novo high levels of triacylglycerides (TAG). In order to achieve high TAG accumulation, Y. lipolytica has high flux through the fatty acid (FA) precursor, acetyl-CoA. In particular, this organism has adapted to funnel >80% of its glycolytic flux to acetyl-CoA production through the activity of pyruvate dehydrogenase (PDH) (Morgunov et al. 2004). However, this reaction takes place in the mitochondria, whereas cytosolic acetyl-CoA is required for FA biosynthesis. As a result, Y. lipolytica utilizes a citrate-malate antiport (Evans et al., 1983) coupled to several related enzymes (citrate synthase, CS; ATP-citrate lyase, ACL; malate dehydrogenase, MDH) to formulate a cycle which interconverts between the two pools of acetyl-CoA. A full description of this transport cycle (Figure 3) can be found in Wasylenko et al., 2015. This shuttling of acetylCoA across the mitochondrial membrane directly competes with the activity of the citric acid (TCA) cycle, a problem that is resolved through nitrogen regulation (Marchal et al., 1977; Ratledge and Wynn, 2002). In particular, when the cells have access to nitrogen, high ATP demands from protein synthesis and DNA replication drive the TCA cycle to operate at high capacity, oxidizing most of the acetyl-CoA. Conversely, when nitrogen is depleted, cellular AMP levels decrease (Boulton and Ratledge, 1983), leading to a decline in isocitrate dehydrogenase (IDH) activity (Botham and Ratledge, 1979), impedance of TCA cycle flux, and accumulation of mitochondrial citrate. The excess citrate is then used to export acetyl-CoA out of the mitochondria. More recently, it has been reported that increased dissolved oxygen levels in Y. lipolytica cultures lead to both a decrease in IDH activity and an increase in citrate production (Bellou et al., 2014), corroborating the proposed mechanism of acetyl-CoA shuttling. This

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method of cytosolic acetyl-CoA generation is distinct from that of non-oleaginous yeasts, which typically use the pyruvate dehydrogenase bypass pathway (Beopoulos et al., 2011). Once transported into the cytosol, acetyl-CoA serves as the basic two-carbon building block for FA elongation and ultimately lipid synthesis. The enzymatic steps of these two processes are similar to that of most eukaryotic organisms and are summarized in Figure 3. Several comprehensive reviews on these topics have been published (Ratledge and Wynn, 2002; Coleman and Lee, 2004; Athenstaedt and Daum, 2006; Tehlivets et al., 2007), we here present the points that are of particular interest to the engineering of Y. lipolytica. Acetyl-CoA carboxylase (ACC1) catalyzes the first step in FA synthesis, converting acetyl-CoA to malonylCoA and priming it for subsequent chemistry as the basic elongation unit: acetyl-CoA + HCO3 + ATP

malonyl-CoA + ADP + Pi

Overexpression of this enzyme in Y. lipolytica has been shown to enhance FA flux (Tai and Stephanopoulos, 2013) and hence this step has been proposed to be rate limiting in the overall FA biosynthesis scheme (Pfleger et al., 2015). FA synthesis is mediated by the fatty acid synthase (FAS) complex where the two subunits, FAS1 and FAS2, catalyze the following reaction with NADPH as the reducing cofactor: acetyl-CoA + n malonyl-CoA + 2n NADPH

Cn+2-acyl-CoA + n CO2 + 2n NADP + n

CoASH For Y. lipolytica, the chain length for naturally synthesized fatty acids is typically 16 or 18 carbons (i.e., n = 7 or 8), yet this pathway is highly regulated to maintain homeostasis (Masoro, 1965; Goodridge, 1972; Heath et al., 1996; Davis and Cronan, 2001). Several studies have sought to alleviate this inhibition through either sequestering FAs into lipids (Tai and Stephanopoulos, 2013) or increasing the proportion of unsaturated FAs (Qiao et al. 2015). Both

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of these engineering methods have demonstrated success in increasing lipid production. Finally the synthesized FA molecules condense with glycerol-3-phosphate (glyc3p) to produce TAGs stored in the lipid body: 3 FA + glyc3p

TAGs

Availability of the FAS cofactor, NADPH, is crucial for lipid synthesis. Engineering redox cofactor availability has recently been shown to enhance lipid yields significantly (Qiao et al. 2017). Unlike most other oleaginous yeasts where NADPH is produced primarily through the activity of malic enzymes (Wynn et al., 1997; Zhang et al., 2007), many studies suggest that Y. lipolytica regenerates NADPH almost exclusively through the oxidative pentose phosphate pathway (PPP) (Wasylenko et al., 2015). Zhang et al. reported that overexpressing the native copy of malic enzyme in Y. lipolytica did not increase its lipid production capabilities and that the enzyme prefers NAD+ over NADP+ (Zhang et al., 2013). Metabolic flux analysis performed on Y. lipolytica suggests a tight correlation between oxidative PPP and lipogenic fluxes, with minimal contribution from other candidate NADPH generating enzymes (Wasylenko et al., 2015; Liu et al., 2016). Furthermore, studies that focused on enhancing the oxidative PPP flux through overexpression of related enzymes have shown an increase in lipid synthesis, further strengthening this argument (Silverman et al., 2016; Yuzbasheva et al., 2017b). In addition to fatty acid biosynthesis, production of the TAG backbone chemical, glyc3p is also key to the lipogenic properties of Y. lipolytica. The enzyme, glyc3p dehydrogenase (GPDH) catalyzes the formation of glyc3p from the glycolytic intermediate, dihydroxyacetone (DHAP) (Beopoulos et al., 2008) and one study (Theirry et al.) suggests that this glyc3p production is key to this organism’s oleaginous traits (Dulermo and Nicaud, 2011). Apart from glyc3p synthesis, recent experimental evidence has also pointed to a tight correlation between

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amino acid synthesis and lipid synthesis, in particular leucine metabolism was shown to have the greatest affect on lipid accumulation (Kerkhoven et al., 2016; Kerkhoven et al., 2017). Finally, the mechanisms of lipid catabolism in Y. lipolytica are also key to understanding the lipid overaccumulation phenotype (Fickers et al., 2005). Degradation of TAGs requires initial mobilization into FAs through the action of two lipid body bound lipases, TGL3 and TGL4 (Beopoulos et al., 2011). The FAs can then be activated to acyl-CoAs by acyl-CoA synthetase and subsequently broken down into two-carbon units through beta-oxidation (Tenagy et al., 2015). Beta-oxidation of acyl-CoAs occurs in a cyclic manner involving four enzymatic steps: acyl-CoA oxidase (Pox1-6), hydratase and dehydrogenase (both performed by the multifunctional enzyme Mfe2), and 3-ketoacyl-CoA thiolase (POT1) (Berninger et al., 1993; Wang et al., 1999; Blazeck et al. 2014). The degradation of FAs is typically carried out in the peroxisome of Y. lipolytica (Dulermo and Nicaud, 2011; Blazeck et al., 2014).

5.2 Metabolic engineering for lipid production Based on these native traits and capacity, it is not surprising that Y. lipolytica has been a host organism for engineered accumulation of lipids, especially in the form of TAGs. A variety of approaches have been used to optimize lipid production and we discuss these here briefly (additionally, these strategies are highlighted in Table 1). As examples, Liu et al. used a rationally engineered strain of Y. lipolytica (Blazeck et al., 2014) and identified a mutant allele in the lipid regulator mga2 that further improved lipid titer to nearly 40 g/L with saturated cells containing upwards of 90% dry cell weight lipid (Liu et al., 2015a). In their recent paper, Xu et al. tested several different pathways for cytosolic acetyl-CoA production in order to promote lipid accumulation. From this study (complementing an ACC1 and DGA1 overexpression strain

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(Tai and Stephanopoulos, 2013)), it was determined that heterologous expression of the S. cerevisiae peroxisomal carnitine acyltransferase led to the highest lipid accumulation, producing 66.4 g/L TAGs using minimal media bioreactor fermentation (Xu et al., 2016). Another strategy combined a knockout of the peroxisomal biogenesis factor, PEX10 with overexpression of the native genes encoding glucose-6-phosphate dehydrogenase and acyl-CoA binding protein yielding a cell with lipid content of 30% dry cell weight (Yuzbasheva et al., 2017b). In a similar fashion, a beta-oxidation knockout combined with overexpressions of DGA2 and GPD1 yielded lipid at 55% dry cell weight under nitrogen limited conditions (Lazar et al., 2014; Sagnak et al., 2018). A different rational engineering approach employed overexpression of native oxidation stress related genes as well as those from the model organisms E. coli and S. cerevisiae leading to the production of 72.7 g/L lipid (Xu et al., 2017). Another study produced lipids at a titer of 98.9 g/L through heterologous expression of glycolytic enzymes that produce NADPH rather than NADH, this cofactor can then be used to promote lipid production (Qiao et al., 2017). Finally, through a recent transcriptomic analysis, researchers have identified a key native regulatory gene, MHY1 (Wang et al., 2017) that can increase lipogenesis when deleted leading to a content of 43.1% dry cell weight. Efforts in the field have been made to expand beyond the TAG production and leverage this host for the direct production of fatty acids. For example, fatty acids can be produced through the expression of a hybrid fatty acid synthase fused to a truncated version of E. coli thioesterase. Using this approach with unsupplemented, minimal medium, it was possible to produce 9.67 g/L of free fatty acids (Xu et al., 2016). An alternative approach to stimulate free fatty acid production involves inhibition of glycerol metabolism and beta-oxidation through a triple knockout strategy. Following evolution to derepress growth and oleate resistance, this

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strain was led to fatty acid secretion at levels of 2.03 g/L following test tube fermentation when combined with overexpression of acetyl-CoA carboxylase and dodecane supplementation (Yuzbasheva et al., 2017a). Further modified products including fatty acid methyl esters have been produced. Production of 142.3 mg/L fatty acid ethyl esters was achieved through the combined expression of Acinetobacter baylyi wax-ester synthase with the lipogenic S. cerevisiae peroxisomal carnitine acyltransferase (Xu et al., 2016). Finally, wholesale shifts in the profile of fatty acids to long chain (C16 and C18) dicarboxylic acids at titers of 3.49 g/L (Abghari et al., 2017) have been achieved via the deletion of degradation pathways and coupled overexpression of omega-oxidation pathway genes. One of the larger, well-known rewiring of lipid production in Y. lipolytica is the engineering by DuPont to produce the nonnative fatty acid, omega-3-eicosapentaenoic acid (EPA), the main nutrient in fish oil. This effort involved introducing multiple heterologous copies of the Δ9-elongase and Δ8, Δ5 and Δ17-desaturase genes from a variety of algae and oomycete species (Xie et al., 2015). The combination of these complementations along with modification of native genes including overexpression of a C16/18 elongase gene and Δ12desaturase gene as well reduction of peroxisome biogenesis through a pex10 modification, led to a final strain able to accumulate EPA at 25% dry cell weight. This final strain is used to produce the New Harvest™ fish oil supplement as well as the source of feed for the EPA rich product, Verlasso® salmon. In similar fashion, Y. lipolytica has been engineered for the production of other nonnative fatty acid products including ricinoleic acid. This valuable product has been produced through the addition of a nonnative ∆12 hydroxylase gene in an engineered strain leading to ricinoleic acid production at 43% of total lipid content (Beopoulos et al., 2014). It is important to

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note that this production was aided by supplementation with 2% ricinoleic acid to stimulate production. Ricinoleic acid from castor oil and other sources has likewise been fed to Y. lipolytica for bioconversion into gamma-decalactone where it has reached as high as 9.4 g/L titers depending on medium and fermentation conditions (Braga and Belo, 2014; Pagot et al., 1997; Wache et al., 1998). Another specialty fatty acid, gamma-linoleic acid has been produced at titers of 71.6 mg/L through the heterologous expression of a nonnative desaturase gene (Sun et al., 2017). The production of conjugated fatty acids (specifically conjugated linoleic acid) has been produced with titers of 302 mg/L through complementation of a linoleic acid isomerase along with reduced TAG formation and soybean oil feeding (Imatoukene et al., 2017). Finally, the production of arachidonic acid has been achieved (albeit at only 0.4% of total fatty acids) through a similar strategy (Liu et al., 2017a). In addition to these fatty acid products, the fuel hydrocarbon, pentane, has also been produced in the lipid accumulation MFE1 knockout background through heterologous expression of a soybean lipoxygenase enzyme (Blazeck et al., 2013a). Collectively, these studies show the flexibility and malleability of the lipid biosynthetic pathways in Yarrowia.

6. Recent advances in metabolic engineering Both industrial and lab-scale productions of chemicals beyond lipids and fatty acids have been explored for Y. lipolytica. In this section, we highlight the metabolic engineering efforts and feats that have been accomplished in this organism to expand the overall chemical profile of this promising host. In addition, to lower the industrial fermentation cost alternative carbon sources have been investigated through the metabolic engineering of catabolic pathways. These efforts are further summarized in Table 1.

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6.1 Organic acids derived from the TCA cycle The overall oleaginous nature of Y. lipolytica and its metabolism as described above also means that this host has a high potential to produce TCA cycle intermediates. This ability is most pronounced in industrial production of citric acid using Y. lipolytica. In recent work (Kamzolova and Morgunov, 2017), a screening of 43 different wild isolates of different yeast species was evaluated for citric acid production. The best isolate belonged to Y. lipolytica and produced 85 g/L of citric acid in minimal medium with a yield of 0.70 g/g. Another recent study of note focused on transcriptome and fluxome characterization in a citrate producing strain ACA DC 50109 (Sabra et al., 2017). In addition to providing a valuable data set, this work was able to improve citrate production leading to 55 g/L from glucose alone and 50 g/L when using a dual substrate medium containing glycerol. Beyond citrate, this organism has been explored for the production of other related organic acids such as succinic acid, alpha-keto glutaric acid, and pyruvic acid. For example, an initial strategy was used to pair a high alpha-ketoglutaric acid producer with a two-step process whereby the alpha-ketoglutaric acid was then converted to succinic acid through a chemical hydrogen peroxide treatment (Kamzolova et al., 2009). More recent work has established a purely biological route to succinic aid through deletion of succinate dehydrogenase and CoAtransferase genes and overexpression of TCA cycle genes to enable 110.7 g/L of succinic acid production (Cui et al., 2017). However, these initial fermentations are limited by a compromised glucose consumption and thus glycerol was used as the carbon source. In order to enable production of succinic acid from fruit and vegetable waste hydrolysate (a cheap and glucose-rich material) a succinate dehydrogenase knockout strain was evolved for restored glucose

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consumption, leading to production of 140.6 g/L of succinic acid (Li et al., 2018; Yang et al., 2017). In a similar manner, pyruvic and alpha-keto-glutaric acids have also been produced through the selection of a wild isolate followed by random mutagenesis and medium optimization leading to the production of 67.4 g/L alpha-keto glutaric acid and 39.1 g/L pyruvic acid (Zeng et al., 2015; Zeng et al., 2016; Zeng et al., 2017). Additionally, a thiamine auxotrophy was found to improve pyruvic acid production leading to production of 61.3 g/L (Morgunov et al., 2004). This work has been expanded to include the production of the nonnative, organic acid itaconic acid. This compound is a valuable platform chemical that can be converted to monomers for a variety of petroleum-derived products, including plastic monomers. Itaconic acid production in Y. lipolytica was enabled through heterologous expression of the Aspergillus terreus cis-aconitic acid decarboxylase. Production was further improved through cytosolic localization of the upstream, native aconitase enzyme, leading production of 4.6 g/L in bioreactor fermentation (Blazeck et al., 2015). Collectively, these results demonstrate the promise of using Y. lipolytica as a host for organic acid production.

6.2 Sugar products There is a high interest in the production of biorenewable sugar alcohols for use as food sweetners. In this regard, Y. lipolytica has been used in fermentations due to its capacity to readily produce erythritol. Substantial erythritol titers (170 g/L) were achieved through the identification of an isolate that does not produce citric acid as an overflow product when fermenting crude glycerol (Rymowicz et al., 2009). A bottom up approach to engineering involving overexpression of the pentose phosphate pathway transketolase gene or a predicted

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erythrose reductase led to more modest titers (62.5 g/L and 78.1 g/L respectively) (Janek et al., 2017; Mirończuk et al., 2017). This pathway has also been exploited for the bioconversion of erythritol into the valuable product erythrulose, a valuable precursor to bioactive compounds including glyceraldehyde acetonide. Through overexpression of erythritol dehydrogenase and knockout of erythrolose kinase, this bioconversion yield reached 0.64 g/g (Carly et al., 2017; Trassaert et al., 2017).

6.3 Nonnative, non-lipid products derived from high acetyl-CoA flux The oleaginous nature of Y. lipolytica effectively stems from a high flux through acetylCoA and malonyl-CoA that can be diverted into an array of valuable compounds. The production of terpenoids is typically achieved through overexpression of the key native mevalonate pathway genes HMG1 and/or GGS1. When these strains are combined with heterologous pathways, modest titers of terpenoids have been produced. The complementation of phytoene synthase, phytoene dehydrogenase, and lycopene cyclase through expression of CarB and the bifunctional CarRP genes from Mucor circinelloides led to 2.22 mg/g dry cell weight beta-carotene (Gao et al., 2014). A multi-functional gene, carS from Schizochytrium sp. that encodes for all three functions led to 0.41mg/g dry cell weight (Gao et al., 2016a; Gao et al., 2014; Gao et al., 2017a). Further engineering efforts in these strains including overexpression of the mevalonate pathway gene ERG10 and knockout of associated beta-oxidation pathways led to the production of 4 g/L beta-carotene (Gao et al., 2017b). In another study, multiple copies of the biosynthetic genes (carB, carRP, and GGS1) were complemented into a variety of lipid overproducing strains and screened for beta-carotene production (Larroude et al., 2018). This study successfully achieved 6.5 g/L beta-carotene production during a bioreactor fermentation.

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In a similar fashion, lycopene production draws from the same mevalonate pathway and thus similar strategies have been employed for lycopene overproduction. For example, by complementing a synthesis pathway from Pantoea ananatis into a high lipid producing strain, researchers achieved 16mg/g dry cell weight lycopene production (Matthäus et al., 2014). Likewise, production of lycopene was enhanced through FBA guided media optimization leading to bioreactor production of 242 mg/L lycopene (Nambou et al., 2015). Finally, as a demonstration of a CRISPR-Cas9 mediated, markerless gene integration strategy, a strain ultimately producing 21.1 mg/g dry cell weight lycopene in bioreactor fermentation was established (Schwartz et al., 2017a; Schwartz et al., 2017b). Beyond carotenoids, other terpenoids of interest have been explored in Y. lipolytica including the production of a downstream carotenoid product, astaxanthin (used as a food and feed additive) through the introduction of an additional gene from Pantoea ananatis, resulting in 54.6 mg/L in microtiter plate fermentation (Kildegaard et al., 2017). In another study, the αfarnesene synthase gene was fused to ERG20, yielding 260mg/L of the biofuel precursor (Yang et al., 2016). Finally, the key fragrance molecule, linalool, has also been produced in Y. lipolytica through melovanate pathway overexpression and heterologous expression leading to 6.96 mg/L linalool production (Cao et al., 2017). A variety of additional non-lipid and non-terpenoid products have been produced in Y. lipolytica. In a proof of concept paper describing a YaliBricks assembly technique, the pathway for violacein biosynthesis from Chromobacetrium violaceum was incorporated into Y. lipolytica to yield 31.5 mg/L production of this antibiotic pigment (Wong et al., 2017). Riboflavin overproduction was demonstrated in conjunction with a droplet-based sorting approach for secreted products (Wagner et al., 2018a). Specifically, this work led to 96 mg/L of riboflavin

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after overexpression of RIB1 and RIB3 as well as knockout of beta-oxidation and subsequent EMS mutagenesis and droplet selection for an improved secretion phenotype (Wagner et al., 2018a). On the biopolymer side, the production pathway for poly-3-hydroxybutyrate from Ralstonia eutropha was incorporated into a strain of Y. lipolytica to result in nearly 7.35 g/L titers of this biodegradable plastic (Li et al., 2017). Finally, through the extensive rewiring of native metabolism along with the introduction of the Gerbera hybrida gene, g2ps1 (Markham et al., 2018), high level production of the polyketide, triacetic acid lactone was produced in Y. lipolytica. Specifically, overexpression of the pyruvate bypass pathway and the peroxisome biogenesis factor, PEX10 both allowed TAL production to become uncoupled from lipid accumulation. These results led to nearly 36 g/L of this polyketide, which can serve as a platform molecule and plastics precursor (Markham et al., 2018). In total, these studies all demonstrate the potential of rewiring the innate flux of acetyl-CoA and malonyl-CoA away from lipids and toward novel products.

6.4 Metabolic engineering to expand carbon source utilization To complement the product engineering approaches above and enable more biosustainable production, unique carbon sources have been explored for Y. lipolytica. For example, a variety of waste media sources have been tested for lipid production. In this regard, lipids can be produced from a wide variety of hydrophobic and hydrophilic carbon sources (Ledesma-Amaro and Nicaud, 2016) as well as waste carbon sources both glucose rich substrates such as various vegetable hydrolysates (Niehus et al., 2018; Quarterman et al., 2017) and oil rich substrates such as waste cooking oil (Katre et al., 2017). Human urine has also been explored as a novel, waste nitrogen source (Brabender et al., 2018).

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Overall, hexose sugars such as glucose and fructose support rapid and robust growth of Y. lipolytica (Qiao et al., 2017; Lazar et al., 2014). Like many other yeasts, glucose is the preferred substrate and inhibits the uptake of most other carbon sources (Petit and Gancedo, 1999; Fickers et al., 2005). However, high concentrations of glucose (>300 g/L) impairs growth, presumably due to osmotic stress, thus requiring fed-batch cultures to achieve high product final titers (Qiao et al., 2015). Growth on other sugar-based components (like molasses) has been enabled by the expression of invertase activity such as through expression of S. cerevisiae SUC2 (Lazar et al., 2011; Hong et al., 2012; Lazar et al., 2013). In such strains, products including lipids and citric acid have been synthesized on sucrose or molasses as the sole carbon source (Moeller et al., 2013; Rakicka et al., 2015; Hapeta et al., 2017). Apart from sugars, Y. lipolytica has been extensively studied for its conversion of glycerol and crude glycerol obtained directly from industrial waste streams (Papanikolaou et al., 2002; Papanikolaou and Aggelis, 2002; Rymowicz et al., 2008; Rywińska et al., 2013). Catabolism of glycerol proceeds primarily through the “G3P pathway”, involving glycerol kinase (GK) encoded by GUT1 and glyc3p dehydrogenase (G3PDH) encoded by GUT2 (Dulermo and Nicaud, 2011; Mironczuk et al., 2016). Surprisingly, glycerol is one of the most preferred carbon sources for Y. lipolytica as it can repress the uptake of many other substrates (which glucose cannot), and in some cases even glucose (Mori et al., 2013; Sabra et al., 2017). This characteristic distinguishes Y. lipolytica from many other yeasts that regard glycerol as a nonfermentative carbon source. Y. lipolytica also has the distinctive feature of using hydrophobic substrates including alkanes and lipids. Once inside the cell, alkanes can be oxidized by a cytochrome P450dependent alkane monooxygenase system (Fickers et al., 2005) prior to entering the beta-

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oxidation pathway. Likewise, the use of volatile fatty acids (VFAs) (such as acetic, propionic, and butyric acid) as a carbon source has been explored (Lian et al., 2012; Jiang et al., 2013; Pfleger, 2016). Owing to membrane diffusion of VFAs in the acidic form, a properly monitored pH can enabled continued uptake (Liu et al., 2016). Final efforts have been made to improve utilization of media components in ligocellulosic materials. For example, utilization of xylose was enabled by the addition of xylose reductase (XYR), xylitol dehydrogenase (XDH), and xylulose kinase (XYK) (Li and Alper, 2016). A blend of native an heterologous enzymes in this pathway have been explored (Li and Alper, 2016; Rodriguez et al., 2016). Other engineering efforts have enabled the expression of βglucosidases to enable direct growth on cellobiose (Guo et al., 2015). Additionally, the introduction of a cellodextrin transporter and an intracellular β-glucosidase both from Neurospora crassa can enable direct production of citric acid from cellobiose (Lane et al., 2015). Finally, recent advances have enabled the co-expression of six cellulolytic enzymes to create a pathway for simultaneous saccharification and fermentation directly from cellulose (Guo et al., 2017). By providing both a rapidly expanding product palette, as well as the ability to consume cheap waste products, Y. lipolytica allows for economical and biorenewable production of a wide range of products.

7. Perspectives The oleaginous industrial workhorse, Y. lipolytica, has long been observed to accumulate high levels of intracellular lipids. The proliferation of efficient genetic tools, screening systems and informative models has allowed metabolic engineers to not only improve overall lipid yields in this organism, but also to expand production to alternative, specialized lipid products. Y.

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lipolytica has a robust history as an industrial producer of citric acid due to its innate ability to produce high levels of TCA cycle intermediates, and engineering efforts have likewise expanded and rerouted this capacity. Building upon this history, many recent studies demonstrate the ability of Y. lipolytica to serve as an ideal host for the production of heterologous secondary metabolites derived from acyl-CoA precursors. In addition to unique biochemistry and native physiology, Y. lipolytica lends itself to facile consumption of many waste products, thus providing access to diverse carbon sources and increasing the economic viability of industrial processes based on this host. The metabolic engineering work reviewed here demonstrates that Y. lipolytica is an attractive industrial host. However, there are many challenges left to be solved to harness the full potential of this lipogenic yeast. Looking to the future, further investment in expanding the genetic tools available for the rapid and efficient engineering of this host is necessary. In particular, stable episomal plasmids will allow for rapid genotype testing and enable researchers to leverage advances in protein engineering and rapid genetics. Although CRISPR technology recently showed high efficiencies in genome editing at a number of chromosomal loci in Y. lipolytica, some improvements of this technology are necessary to enable genome-wide efficiency. From a systems biology point of view, increased multi-omics data collection and an enhanced genome annotation will likewise yield useful databases and insightful metabolic models. With these improved technical and informational resources, it is anticipated that researchers will continue to expand beyond the view of Y. lipolytica as cell factory for lipids to become a cell factory for added-value lipid-derivatives as well as for diverse heterologous specialty chemicals. These limitations notwithstanding, the exciting research of the past several years provides a strong foundation for future work; with the application of emerging tools and

35

systems biology knowledge researchers will be able to unlock the potential of Y. lipolytica to serve as an industrial powerhouse. Acknowledgements This work was funded through the Office of Naval Research (ONR) under grant N00014-15-12785 (to H.A.), the Welch Foundation under grant F-1753 (to H.A.) and the Department of Energy under grant DE-SC0008744 (to G.S.). A. M. A. was funded by a postdoctoral fellowship from the Natural Sciences and Engineering Research Council of Canada (NSERC) under fellowship number PDF-488195-2016, and partly by the US DoE grant DE-SC0008744 mentioned above. We also thank the Koch Institute Swanson Biotechnology Center for technical support, specifically the Microscopy Core. Figure 1. Macroscopic appearance of Y. lipolytica colonies. Colonies of Y. lipolytica Po1f strain grown on (A) yeast-peptone-glucose (YPD) solid medium at 30 °C for 4 days, showing the convoluted white surface, (B) YPD at 30 °C for 2 days, showing the pale matte surface (C) YPD at 30 °C for 2 days then kept at 4 °C for 1 week, showing the smooth surface.

Figure 2. Lipid bodies of Y. lipolytica visualized using fluorescence microscopy. Cells were cultivated in YNB media grown under high carbon to nitrogen ratio. Cells were then stained using Nile Red stain and imaged by DIC and epifluorescence with a Nikon TE2000 microscope, equipped with a Lumencor, Spectra-CS, a 100 X, NA 1.49 oil objective, a red filter excitation /emission cube 530-560/590-650 (exposure time of 4 ms), an Andor Zyla camera. Lipid bodies appear as the red spots inside the cells.

36

Figure 3. Overview of the primary metabolic network in Y. lipolytica. Each color represents a different pathway: blue, glycolysis; green, pentose phosphate pathway; orange, TCA cycle; light red, acetyl-CoA shuttle and anaplerotic pathways; yellow, TAG synthesis pathways; dark red, lipid degradation pathways; black, substrate assimilation pathways; grey, major product synthesis pathways. Pathway localization with respect to specific subcellular organelles (represented by purple dotted squares) are also depicted. Arrow thickness in central carbon metabolism (glycolysis, TCA, and PPP) as well as TAG synthesis pathways represent relative pathway flux observed in a WT strain (Wasylenko et al. 2015). Table 1: Summary of many recent genetic strategies for native and nonnative chemical production in Y. lipolitca. For simplicity regulatory elements and copy number information have been removed. Genomic knockouts are represented by Δ followed by lowercase letters. In contrast desaturase and elongase genes are represented through the following notation: Δ-#-D or Δ-#-E. Native overexpressions are represented as all uppercase. All other notation refers heterologous genes or mutant alleles, following the notation used by the referenced study.. Referenc es

Product

Mediu Scale m

Backgr ound

Genetic Modifica tions

Evoluti on

Titer

ribofla vin product ion

96 mg/L

Native Products (Wagner et al., 2018a)

riboflavin

define d

flask

PO1f

Δpex10, URA3, RIB1, RIB3

(Gajdoš et al., 2017)

fatty acid

compl ex, glycer ol

flask

W29

Δdga1, Δdga2, Δlro1, Δare1, DGA2

37

9.9g/L

Yield

Rate

(Qiao et al., 2017)

fatty acid methyl esters

compl ex

biore actor

PO1g

LEU2, ACC1, DGA1, URA3, MCE, GapC

98.9 g/L

(Wang et al., 2016)

fatty alcohol

minim al

biore actor

PO1f

Tafar1, Δdga1, Δfao1

690.21 mg/L

(Yuzbash eva et al., 2017a)

free fatty acid minim al, glycer ol and dodec ane

flask

W29

Δgpd1, Δgut2, Δpex10, ACC1

(Xu et al., 2016)

free fatty acids

minim al

biore actor

PO1g

hybrid FAS1EcTesAtr uncated

9.67 g/L

(Abghari et al., 2017)

long chain dicarboxylic acids (16 and 18)

define d, glycer ol

biore actor

H222

Δpox1-6, Δsnf1, Δfaa1, ALK5, CPR, FAO1, ALK5

3.49 g/L

(Hanko et al., 2018)

long methyl ketones

compl ex

biore actor

PO1f

Δpot1, fadMSKL, fadBSKL, acoSKL

314.8 mg/L

(Blazeck et al., 2014)

triaceylglycer ides

minim al

biore actor

PO1f

Δpex10, Δmfe1, LEU2, URA3, DGA1

25.3g/ L

(Brabend er et al., 2018)

triaceylglycer ides

define d, urine

flask

PO1f

LEU2

0.653 g/L

38

glycero l derepre ssion, oleate resistan ce

1.3 g/L/h

0.018 g/g

2.03 g/L

0.06g/ g

0.04g/ L*h

(Katre et al., 2017)

Triaceylglyce rides

compl ex, waste cookin g oil

flask

NCIM 3589

random mutagene sis, selected for lipid productio n

(Liu et al., 2015b)

triaceylglycer ides

minim al

biore actor

PO1f

Δpex10, Δmfe1, LEU2, URA3, DGA1

(Liu et al., 2015a)

triaceylglycer ides

minim al

biore actor

PO1f

high lipid random mutant, DGA1, URA3, LEU2

25g/L

(Niehus et al., 2018)

triaceylglycer ides

agave bagass e hydrol ysate

biore actor

PO1d

Δpox1-6, Δtgl4, GDP1, DGA2, XR, XDH, XK, AnXPKA, AnACK

16.5g/ L

(Qiao et al., 2015)

triaceylglycer ides

compl ex

biore actor

PO1f

URA3, ACC1, DGA1, SCD

55g/L

(Quarter man et al., 2017)

triaceylglycer ides

switch grass hydrol ysate

flask

YB-420

(Lazar et al., 2014; Sagnak et al., 2018)

triaceylglycer ides

minera biore l actor

39

PO1f

Δtgl4, Δpox1–6, LEU2, DGA2, URA3, GPD1

67% DCW

random mutage nesis, selecte d for lipid content

39.1g/ L

0.56g/l /h

3.44g/ g sugars

1.85g/ L/h

1g/l/h

37.9 g/L

0.152 g/g

55% DCW

0.14g/ g

(Tai and Stephano poulos, 2013)

triaceylglycer ides

compl ex

biore actor

PO1f

URA3, ACC1, DGA1

61.4% DCW

(Wang et al., 2017)

triaceylglycer ides

compl ex

flask

ACADC 50109

Δmhy1

43.1% DCW

(Xu et al., 2016)

triaceylglycer ides

minim al

biore actor

PO1g

ACC1, DGA1, ScperCat 2

(Xu et al., 2017)

triaceylglycer ides

minim al

biore actor

PO1g

(Yuzbash eva et al., 2017b)

triaceylglycer ides

minim al

flask

W29

(Kamzol ova and Morguno v, 2017)

citric acid

minera biore l actor

VKM Y-2373

85 g/L

(Sabra et al., 2017)

citric acid

compl ex

biore actor

ACA DC 50109

55 g/L

(Morgun ov et al., 2004)

pyruvic acid

minim al, glycer ol

biore actor

VKM374/4

61.3 g/L

(Cui et al., 2017)

succinic acid

compl ex, glycer ol

biore actor

PO1f

Δsdh5, Δach1 ScPCK SCS2

110.7 g/L

(Gao et al., 2016b)

succinic acid

compl ex, crude glycer ol

biore actor

PO1f

∆sdh5, URA3

160g/L

40

0.195 g/g

0.143g /L/h

66.4 g/L

0.229 g/g

0.565 g/L/h

ACC1, DGA1, URA3, EcAldH, ScZWF, GSR, GPO

72.7 g/L

0.252 g/g

0.97 g/L/h

Δpex10, ZWF1, ACBP

30% DCW 0.70 g/g

0.71g/ g

76.3m g/g DCW/ h

(Kamzol ova et al., 2009)

succinic acid

minim al, ethano l

biore actor

VKM Y-2412

(Li et al., 2018; Yang et al., 2017)

succinic acid

fruit and vegeta ble waste hydrol ysate

biore actor

PO1f

∆sdh5, URA3

(Guo et al., 2014)

α keto glutaric acid

minim al, glycer ol

biore actor

WSHZ06

PDA1

43.3g/ L

(Yovkov a et al., 2014)

α keto glutaric acid

crude glycer ol

biore actor

H355

PYC1, IDP1

186g/L

(Zhou et al., 2012)

α keto glutaric acid

minim al, glycer ol

biore actor

WSHZ06

MmACL

56.5g/ L

(Zeng et al., 2015; Zeng et al., 2016; Zeng et al., 2017)

α keto glutaric acid and pyruvic acid

minim al, glycer ol

biore actor

WSHZ06

random mutagene sis, screened for low pH

67.4 g/L α keto glutari c acid, 39.1 g/L pyruvi c acid

(Janek et al., 2017)

erythritol

compl ex, glycer ol

biore actor

A101

ER

78.1 g/L

1 g/L/h

(Mirończ uk et al., 2017)

erythritol

compl ex, glycer ol

biore actor

MK1

TKL1

62.5 g/L

0.62g/ Lh

(Rymowi cz et al., 2009)

erythritol

crude glycer ol

biore actor

Wratisl avia K1

41

63.4 g/L

restore d glucose consum ption

58% EtOH fed

140.6 g/L

170g/L

0.69 g/L/h

3.7g/g

0.71 g/g

56%

0.74 g/L/h

1g/L/h

(Carly et al., 2018; Carly et al., 2017)

erythrulose

compl ex, erythri tol

biore actor

(Rakicka et al., 2017)

polyols

molass biore es, actor crude glycer ol

PO1d

Δeyk1, EYD1, URA3

Wratisl avia K1

Δura3, ScSUC2, GUT1

82.2 g/L erythri tol, 7.5 g/L arabito l, 11 g/L mannit ol

0.64 g/g

0.116g /g DCW* h

1.09 g/g

0.67g/ L/h

Nonnative Products (Wong et al., 2017)

violacein

define d

flask

W29

(Braga and Belo, 2014)

gammadecalactone

compl ex, castor oil

biore actor

W29

(Wache et al., 1998)

gammadecalactone

compl flask ex, methyl ricinol eate

PO1d

(Pagot et al., 1997)

gammadecalactone

compl flask ex, methyl ricinol eate

PO1d

(Liu et al., 2017b)

arachidonic acid

compl ex

PO1f

42

flask

vioA, vioB, vioC, vioD, vioE

31.5 mg/L

5.4 g/L

Δaco3

195 mg/L

9.4g/L

URA3, Δ6-D, Δ-6E, Δ-5-D

215 mg/L/ h

0.42% total FA

0.58g/ g methy l ricino leate

13.6m g/g/h

(Imatouk ene et al., 2017)

conjugated linoleic acids

compl biore ex, actor soybea n oil

PO1d

Δpox1-6, Δdga1, Δdga2, Δlro1, Δfad2, FAD2, oPAI

302mg /L

(Imatouk ene et al., 2017)

conjugated linoleic acids

compl ex

biore actor

PO1d

Δpox1-6, Δdga1, Δdga2, Δlro1, Δfad2, FAD2, oPAI

180 mg/L

(Xu et al., 2016)

fatty acid ethyl esters

minim al

flask

PO1g

AbAtfA, ScperCat 2

142.5 mg/L

(Xu et al., 2016)

fatty alcohols

minim al

biore actor

PO1g

Maqu222 0, EcfadD

2.15 g/L

(Xu et al., 2016)

fatty alkanes

minim al

flask

PO1g

MmCAR, BsuSfp, PmADO

23.3 mg/L

(Sun et al., 2017)

gammalinoleic acid

compl ex

flask

PO1f

Δ-6-D

71.6 mg/L

(Gao et al., 2015)

Mediumchain-length polyhydroxya lkanoates

compl ex, triolei n

flask

PO1h

PhaC1 with PTS1 signal and Kozak sequence

1.11g/ L

(Xie et al., 2017)

omega-3 eicosapentae noic acid

compl ex

biore actor

ATCC 20362

Δpex10, Δ-9-E, Δ8-D, Δ-5D, Δ-17D, C1618-E, Δ12-D, acyltransf erase

25% biomas s

43

(Xie et al., 2015)

omega-3 eicosapentae noic acid

compl ex

biore actor

ATCC 20362

Δpex10, Δ-9-E, Δ8-D, Δ-5D, Δ-17D, C1618-E, Δ12-D, acyltransf erase

25% DCW

(Xue et al., 2013)

omega-3 eicosapentae noic acid

compl ex

shake flask

ATCC 20362

Δpex10, Δlip1, Δscp2, Δyali0c18 71, Δ-12D, Δ-9-E, C16/18E, Δ-8-D, Δ-5-D, Δ17-D

15% DCW

(Blazeck et al., 2013a)

pentane

define d

1L bottle s

po1f

Δmfe1, Gmlox1

4.98m g/L

(Beopoul os et al., 2014)

ricinoleic acid

compl ex, ricinol eic acid

tube

PO1d

Δpox1-6, Δdga1, Δdga2, Δlro1, ∆fad2, CpFAH1 2, YlLRO1

43% total lipid

63mg/ g DCW

(Blazeck et al., 2015)

itaconic acid

minim al

biore actor

PO1f

LEU2, URA3, AtCAD, ACOnoM LS

4.6g/L

0.058 g/g

(Li et al., 2017)

poly-3hydroxybutyr ate

compl biore ex, actor acetate

PO1g

URA3, phaC, phaA, phaB

7.35 g/L

44

0.045g /L/h

(Markha m et al., 2018)

triacetic acid lactone

compl biore ex, actor acetate spike

PO1f

g2ps1, LEU2, URA3, ACS1, ALD5, PDC2, ACC1

35.9 g/L

0.164 g/g*

(Markha m et al., 2018)

triacetic acid lactone

define d

tube

PO1f

g2ps1, LEU2, URA3, PEX10

4.1 g/L

0.203 g/g

(Yu et al., 2018)

triacetic acid lactone

minim al

biore actor

W29

URA3, LEU2, g2ps1

2.6g/L

13.80 %

(Zhang et al., 2017)

campesterol

compl ex

biore actor

ATCC 201249

Δerg5, DHCR7, POX2

942 mg/L

(Kildegaa rd et al., 2017)

astaxanthin

compl ex

micro titer plate

GB20

Δmus51, nugmHtg2, ndh2i, XdcrtI, XdcrtYB, XdcrtE, HMG1, PSQS1_50bp: SQS1, PscrtW, PacrtZ

54.6 mg/L

3.5 mg/g DCW

(Cao et al., 2017)

linalool

compl flask ex, citrate and pyruva te

PO1f

ERG20F88 W-N119W , HMG1, IDI1

6.96 mg/L

939 μg/g DCW

(Schwart z et al., 2017a; Schwartz et al., 2017b)

lycopene

compl ex

PO1f

HMG1, CrtE, CrtB, CrtI, LEU2, MVD1, ERG8

45

biore actor

21.1 mg/g DCW

max 0.21 g/L/h

(Nambou et al., 2015)

lycopene

compl ex

biore actor

PO1f

CrtE, CrtB, CrtI

(Matthäu s et al., 2014)

lycopene

minim al

biore actor

H222

ScSUC2, Δpox1-6, Δgut2, CrtB, CrtI, GGS1, HMG1

(Yang et al., 2016)

α-farnesene

compl ex, dodec ane

biore actor

PO1h

tHMG1, hybrid OptFSERG20, IDI

(Gao et al., 2016a; Gao et al., 2014; Gao et al., 2017a)

β-carotene

compl ex

flask

ATCC MYA2613

ScSUC2, Δku70, Δku80, GGS1, βcarS, URA3

0.41m g/g DCW

(Gao et al., 2014)

β-carotene

compl ex

flask

ATCC 201249

GGS1, CarB, CarRP, URA3

2.22 mg/g DCW

(Larroud e et al., 2018)

β-carotene

compl ex

biore actor

Po1D

DGA2, GPD1, Δpox1-6, Δtgl4, GGS1, CarB, CarRP, tHMG1

46

242 mg/L 16 mg/g CDW

259.98 mg/L

6.5 g/L

33.98 mg/g DCW

89.6 mg/g DCW

(Kildegaa rd et al., 2017)

β-carotene

compl ex

micro titer plate

GB20

Δmus51, nugmHtg2, ndh2i, XdcrtI, XdcrtYB, XdcrtE, HMG1, PSQS1_50bp: SQS1

797.1 mg/L

(Gao et al., 2017a)

β-carotene

compl ex

biore actor

ATCC MYA2613

ScSUC2 Δku70, CarB, CarRP Δpox4, ERG10

4 g/L

(Celińska and Grajek, 2013; Celińska et al., 2013)

2phenylethano l

minim al, glycer ol and L-Phe

biore actor

NCYC3 825

ScSUC2, dhaB,  dhaB2, dhaT

1.98g/ L

0.014 g/L/h

0.31g/ g

20mg/ (Lxh)

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Highlights:    

Yarrowia lipolytica is a host for both lipid and nonlipid chemical products Emerging synthetic biology tools enable further engineering in this host Successful engineering for lipid production phenotypes Engineering for both native and nonnative lipid, organic acid, sugar and acetyl-CoA derived products

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